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Unit 2: Discussion Introduction As production manager last unit in the Kibby and Strand simulation you gained insights into how raw materials were turned into finished goods. This unit you will learn more about the front end of the operational process employed by the company. Specifically, you will learn how to manage suppliers who provide the raw materials used in the production of the company’s textile products. Some challenges you will face are: 1) which suppliers provide the best quality raw materials; 2) which suppliers are the most reliable; and 3) which suppliers have the most competitive prices.  The simulation scenario will pose many opportunities for decision making and forecasting, and if you make a poor decision regarding suppliers it will impact the ability of Kibby and Strand to meet its contractual obligations, leading to dissatisfied customers. Since customer satisfaction weighs heavily on future contracts, you can’t simply make the best decision for the moment, but rather the best decision for the long haul. This scenario provides a realistic illustration of the issues textile companies face across the U.S. It’s extremely important that operations professionals have an above average comfortable level when it comes to establishing grounded assumptions and conducting and interpreting financial and operational forecasts. In its simplest form, forecasting is a process that represents an “educated guess”. In business, we use time series methods, the indicator approach, or regression analyses to forecast the nature of a situation or future values. The data we observe when forecasting fall into one of four types: trended patterns, seasonal patterns, cyclical patterns, or irregular patterns (Kros & Brown, 2013). Forecasting models are used to predict consumer demand, which, in turn, aids management in forecasting staffing requirements. In addition, to demand forecasts, management routinely engages in financial forecasting, which includes, but is not limited to: sales growth, economic predictions, and forecast future cash flows. In order to perform forecasts, it’s important that the management team signoff on the underlying assumptions used to complete these analyses, such as population growth and technology development. The following represents the typical steps one undertakes when preparing for and conducting a forecast (Investopedia, n.d.): 1. A problem or data point is chosen. This can be something like "will people buy a high-end coffee maker?" or "what will our sales be in March next year?" 2. Theoretical variables and an ideal data set are chosen. This is where the forecaster identifies the relevant variables that need to be considered and decides how to collect the data. 3. Assumption time. To cut down the time and data needed to make a forecast, the forecaster makes some explicit assumptions to simplify the process. 4. A model is chosen. The forecaster picks the model that fits the data set, selected variables and assumptions. 5. Analysis. Using the model, the data is analyzed and a forecast made from the analysis. 6. Verification. The forecaster compares the forecast to what actually happens to tweak the process, identify problems or in the rare case of an absolutely accurate forecast, pat himself on the back. Sources: Kros, J. F., & Brown, E. (2013). Health Care Operations and Supply Chain Management.  San Francisco, CA: John Wiley & Sons. http://www.investopedia.com/articles/financial-theory/11/basics-business-forcasting.asp (Links to an external site.)  Unit Learning Outcomes 1. Develop a plan for forecasting impacts to an organization’s bottom line. (CLO 1, 2, 4, and 7) 2. Demonstrate how to perform forecasting using data and statistics. (CLO 4 and 5) 3. Identify trends and patterns in data as they apply to forecasting. (CLO 1, 3, 5, and 7) 4. Develop a data collection plan that will permit the creation of an accurate and reliable forecasting model. (CLO 3, 4, and 5) Directions Accessing McGraw-Hill Connect Follow these steps to view the scenario. Initial Posting Go to McGraw-Hill Practice Operations to view the scenario. 1. Click the "McGraw-Hill Connect" tab in the course navigation menu. 2. Click the McGraw-Hill Practice Operations link. Students are to complete Module 3, Forecasting and Contracts (Scenario) in Practice Operations. Based on their observations in this scenario, and upon a careful review of the available literature, the student is to consider him or herself to be the Production Manager of Kibby and Strand, the company in the scenario. Create a forecasting plan to forecast production output for Kibby and Strand.  The plan should include forecasting objectives, the data to be used in forecasting, and the quantitative methods the staff is to use in creating the production output forecast. Instruction Guidance: It would be prudent to consider content covered in chapter 3 of the textbook; however, there are many other useful resources available on the Internet and in the literature to support the construction of your action plan.  This forecasting plan should be prepared as a single Microsoft Word document, and then attached to the unit discussion thread. There is no minimum or maximum in terms of the word count; however, the response should explicitly address all required components of this discussion assignment. The document should be prepared consistent with the APA writing style and reflect higher level cognitive processing (analysis, synthesis and or evaluation). Chapter 3 Forecasting © McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw-Hill Education. 1 Learning Objectives (1 of 2) You should be able to: 3.1 List features common to all forecasts 3.2 Explain why forecasts are generally wrong 3.3 List elements of a good forecast 3.4 Outline the steps in the forecasting process 3.5 Summarize forecast errors and use summaries to make decisions 3.6 Describe four qualitative forecasting techniques 3.7 Use a naïve method to make a forecast 3.8 Prepare a moving average forecast 3.9 Prepare a weighted-average forecast 3-‹#› © McGraw-Hill Education. Learning Objectives (2 of 2) 3.10 Prepare an exponential smoothing forecast 3.11 Prepare a linear trend forecast 3.12 Prepare a trend-adjusted exponential smoothing forecast 3.13 Compute and use seasonal relatives 3.14 Compute and use regression and correlation coefficients 3.15 Construct control charts and use them to monitor forecast errors 3.16 Describe the key factors and trade-offs to consider when choosing a forecasting technique 3-‹#› © McGraw-Hill Education. Forecast Forecast – a statement about the future value of a variable of interest We make forecasts about such things as weather, demand, and resource availability Forecasts are important to making informed decisions 3-‹#› © McGraw-Hill Education. Two Important Aspects of Forecasts Expected level of demand The level of demand may be a function of some structural variation such as trend or seasonal variation Accuracy Related to the potential size of forecast error 3-‹#› © McGraw-Hill Education. Forecast Uses (1 of 2) Plan the system Generally involves long-range plans related to: Types of products and services to offer Facility and equipment levels Facility location 3-‹#› © McGraw-Hill Education. Forecast Uses (2 of 2) Plan the use of the system Generally involves short- and medium-range plans related to: Inventory management Workforce levels Purchasing Production Budgeting Scheduling 3-‹#› © McGraw-Hill Education. Learning Objective 3.1 Features Common to All Forecasts Techniques assume some underlying causal system that existed in the past will persist into the future Forecasts are not perfect Forecasts for groups of items are more accurate than those for individual items Forecast accuracy decreases as the forecasting horizon increases 3-‹#› © McGraw-Hill Education. Learning Objective 3.2 Forecasts Are Not Perfect Forecasts are not perfect: Because random variation is always present, there will always be some residual error, even if all other factors have been accounted for. 3-‹#› © McGraw-Hill Education. Learning Objective 3.3 Elements of a Good Forecast The forecast Should be timely Should be accurate Should be reliable Should be expressed in meaningful units Should be in writing Technique should be simple to understand and use Should be cost-effective 3-‹#› © McGraw-Hill Education. Learning Objective 3.4 Steps in the Forecasting Process Determine the purpose of the forecast Establish a time horizon Obtain, clean, and analyze appropriate data Select a forecasting technique Make the forecast Monitor the forecast errors 3-‹#› © McGraw-Hill Education. Learning Objective 3.5 Forecast Accuracy and Control Allowances should be made for forecast errors It is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs Forecast errors should be monitored Error = Actual – Forecast If errors fall beyond acceptable bounds, corrective action may be necessary 3-‹#› © McGraw-Hill Education. Learning Objective 3.5 Forecast Accuracy Metrics MAD weights all errors evenly MSE weights errors according to their squared values MAPE weights errors according to relative error 3-‹#› © McGraw-Hill Education. Learning Objective 3.5 Forecast Error Calculation Period Actual (A) Forecast (F) (A-F) Error |Error| Error2 [|Error|/Actual]x100 1 107 110 -3 3 9 2.80% 2 125 121 4 4 16 3.20% 3 115 112 3 3 9 2.61% 4 118 120 -2 2 4 1.69% 5 108 109 1 1 1 0.93% Sum 13 39 11.23% n = 5 n-1 = 4 n = 5 MAD MSE MAPE = 2.6 = 9.75 = 2.25% 3-‹#› © McGraw-Hill Education. Learning Objective 3.6 Forecasting Approaches (1 of 2) Qualitative forecasting Qualitative techniques permit the inclusion of soft information such as: Human factors Personal opinions Hunches These factors are difficult, or impossible, to quantify 3-‹#› © McGraw-Hill Education. Learning Objective 3.6 Forecasting Approaches (2 of 2) Quantitative forecasting These techniques rely on hard data Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast 3-‹#› © McGraw-Hill Education. Learning Objective 3.6 Qualitative Forecasts (1 of 2) Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts Executive opinions A small group of upper-level managers may meet and collectively develop a forecast Sales force opinions Members of the sales or customer service staff can be good sources of information due to their direct contact with customers and may be aware of plans customers may be considering for the future 3-‹#› © McGraw-Hill Education. Learning Objective 3.6 Qualitative Forecasts (2 of 2) Consumer surveys Since consumers ultimately determine demand, it makes sense to solicit input from them Consumer surveys typically represent a sample of consumer opinions Other approaches Managers may solicit 0pinions from other managers or staff people or outside experts to help with developing a forecast. The Delphi method is an iterative process intended to achieve a consensus 3-‹#› © McGraw-Hill Education. Time-Series Forecasts Forecasts that project patterns identified in recent time-series observations Time-series – a time-ordered sequence of observations taken at regular time intervals Assume that future values of the time-series can be estimated from past values of the time-series 3-‹#› © McGraw-Hill Education. Time-Series Behaviors Trend Seasonality Cycles Irregular variations Random variation 3-‹#› © McGraw-Hill Education. Trends and Seasonality Trend A long-term upward or downward movement in data Population shifts Changing income Seasonality Short-term, fairly regular variations related to the calendar or time of day Restaurants, service call centers, and theaters all experience seasonal demand 3-‹#› © McGraw-Hill Education. Cycles and Variations (1 of 2) Cycle Wavelike variations lasting more than one year These are often related to a variety of economic, political, or even agricultural conditions Irregular variation Due to unusual circumstances that do not reflect typical behavior Labor strike Weather event 3-‹#› © McGraw-Hill Education. Cycles and Variations (2 of 2) Random Variation Residual variation that remains after all other behaviors have been accounted for 3-‹#› © McGraw-Hill Education. Learning Objective 3.7 Time-Series Forecasting - Naïve Forecast Naïve forecast Uses a single previous value of a time series as the basis for a forecast The forecast for a time period is equal to the previous time period’s value Can be used with A stable time series Seasonal variations Trend 3-‹#› © McGraw-Hill Education. Learning Objective 3.8 Time-Series Forecasting - Averaging These techniques work best when a series tends to vary about an average Averaging techniques smooth variations in the data They can handle step changes or gradual changes in the level of a series Techniques Moving average Weighted moving average Exponential smoothing 3-‹#› © McGraw-Hill Education. Learning Objective 3.8 Moving Average (1 of 2) Technique that averages a number of the most recent actual values in generating a forecast 3-‹#› © McGraw-Hill Education. Learning Objective 3.8 Moving Average (2 of 2) As new data become available, the forecast is updated by adding the newest value and dropping the oldest and then re-computing the average The number of data points included in the average determines the model’s sensitivity Fewer data points used—more responsive More data points used—less responsive 3-‹#› © McGraw-Hill Education. Learning Objective 3.9 Weighted Moving Average The most recent values in a time series are given more weight in computing a forecast The choice of weights, w, is somewhat arbitrary and involves some trial and error 3-‹#› © McGraw-Hill Education. Learning Objective 3.10 Exponential Smoothing A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error 3-‹#› © McGraw-Hill Education. Learning Objective 3.11 Linear Trend A simple data plot can reveal the existence and nature of a trend Linear trend equation 3-‹#› © McGraw-Hill Education. Learning Objective 3.11 Estimating Slope and Intercept Slope and intercept can be estimated from historical data 3-‹#› © McGraw-Hill Education. Learning Objective 3.12 Trend-Adjusted Exponential Smoothing (1 of 2) The trend adjusted forecast consists of two components Smoothed error Trend factor 3-‹#› © McGraw-Hill Education. Learning Objective 3.12 Trend-Adjusted Exponential Smoothing (2 of 2) Alpha and beta are smoothing constants Trend-adjusted exponential smoothing has the ability to respond to changes in trend 3-‹#› © McGraw-Hill Education. Learning Objective 3.13 Techniques for Seasonality (1 of 2) Seasonality – regularly repeating movements in series values that can be tied to recurring events Expressed in terms of the amount that actual values deviate from the average value of a series Models of seasonality Additive Seasonality is expressed as a quantity that gets added to or subtracted from the time-series average in order to incorporate seasonality 3-‹#› © McGraw-Hill Education. Learning Objective 3.13 Techniques for Seasonality (2 of 2) Multiplicative Seasonality is expressed as a percentage of the average (or trend) amount which is then used to multiply the value of a series in order to incorporate seasonality 3-‹#› © McGraw-Hill Education. Learning Objective 3.13 Seasonal Relatives (1 of 2) Seasonal relatives The seasonal percentage used in the multiplicative seasonally adjusted forecasting model Using seasonal relatives To deseasonalize data Done in order to get a clearer picture of the nonseasonal (e.g., trend) components of the data series Divide each data point by its seasonal relative 3-‹#› © McGraw-Hill Education. Learning Objective 3.13 Seasonal Relatives (2 of 2) To incorporate seasonality in a forecast Obtain trend estimates for desired periods using a trend equation Add seasonality by multiplying these trend estimates by the corresponding seasonal relative 3-‹#› © McGraw-Hill Education. Learning Objective 3.14 Associative Forecasting Techniques Associative techniques are based on the development of an equation that summarizes the effects of predictor variables Predictor variables - variables that can be used to predict values of the variable of interest Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms 3-‹#› © McGraw-Hill Education. Learning Objective 3.14 Simple Linear Regression Regression - a technique for fitting a line to a set of data points Simple linear regression - the simplest form of regression that involves a linear relationship between two variables The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion) 3-‹#› © McGraw-Hill Education. Learning Objective 3.14 Least Squares Line 3-‹#› © McGraw-Hill Education. Learning Objective 3.14 Correlation Coefficient (1 of 2) Correlation, r A measure of the strength and direction of relationship between two variables Ranges between -1.00 and +1.00 3-‹#› © McGraw-Hill Education. Learning Objective 3.14 Correlation Coefficient (2 of 2) r2, square of the correlation coefficient A measure of the percentage of variability in the values of y that is “explained” by the independent variable Ranges between 0 and 1.00 3-‹#› © McGraw-Hill Education. Learning Objective 3.14 Simple Linear Regression Assumptions Variations around the line are random Deviations around the average value (the line) should be normally distributed Predictions are made only within the range of observed values 3-‹#› © McGraw-Hill Education. Learning Objective 3.14 Issues to Consider: Always plot the line to verify that a linear relationship is appropriate The data may be time-dependent If they are use analysis of time series use time as an independent variable in a multiple regression analysis A small correlation may indicate that other variables are important 3-‹#› © McGraw-Hill Education. Learning Objective 3.15 Monitoring the Forecast (1 of 2) Tracking forecast errors and analyzing them can provide useful insight into whether forecasts are performing satisfactorily Sources of forecast errors: The model may be inadequate due to omission of an important variable a change or shift in the variable the model cannot handle the appearance of a new variable 3-‹#› © McGraw-Hill Education. Learning Objective 3.15 Monitoring the Forecast (2 of 2) Irregular variations may have occurred Random variation Control charts are useful for identifying the presence of non-random error in forecasts Tracking signals can be used to detect forecast bias 3-‹#› © McGraw-Hill Education. Learning Objective 3.15 Control Chart Construction 3-‹#› © McGraw-Hill Education. Learning Objective 3.16 Choosing a Forecasting Technique Factors to consider Cost Accuracy Availability of historical data Availability of forecasting software Time needed to gather and analyze data and prepare a forecast Forecast horizon 3-‹#› © McGraw-Hill Education. Operations Strategy (1 of 2) The better forecasts are, the more able organizations will be to take advantage of future opportunities and reduce potential risks A worthwhile strategy is to work to improve short-term forecasts Accurate up-to-date information can have a significant effect on forecast accuracy: Prices Demand Other important variables 3-‹#› © McGraw-Hill Education. Operations Strategy (2 of 2) Reduce the time horizon forecasts have to cover Sharing forecasts or demand data through the supply chain can improve forecast quality 3-‹#› © McGraw-Hill Education. 50 End of Presentation © McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw-Hill Education. 3-‹#› n å - = t t Forecast Actual MAD ( ) 2 t t 1 Forecast Actual MSE - - = å n n å ´ - = 100 Actual Forecast Actual MAPE t t t average moving the in periods of Number period in value Actual average moving period MA period time for Forecast where ... MA 1 2 1 = - = = = + + + = = = - - - - = - å n i t A n t F n A A A n A F i t n t t t n t n i i t n t etc. , 1 period for value actual the , period for value actual the etc. , 1 period for weight , period for weight where ) ( ... ) ( ) ( 1 1 1 1 - = = - = = + + + = - - - - - - t A t A t w t w A w A w A w F t t t t n t n t t t t t t period previous the from sales or demand Actual constant Smoothing = period previous the for Forecast period for Forecast where ) ( 1 1 1 1 1 = = = - + = - - - - - t t t t t t t A α F t F F A α F F 0 from periods time of number Specified line the of Slope 0 at of Value period for Forecast where = = = = = = + = t t b t F a t F bt a F t t t ( ) series time the of Value periods of Number where or 2 2 = = - - = - - = å å å å å å å y n t b y n t b y a t t n y t ty n b estimate trend Current error smoothed plus forecast Previous where TAF 1 + = = + = t t t t t T S T S ( ) ( ) 1 1 1 + TAF TAF TAF α + TAF TAF - - - - - + = - = + = t t t t t t t t t t t T β T A S T S 1 t T ( ) ( ) ( ) ( ) ( ) ns observatio paired of Number where or and intercept) the at line the of height the (i.e., 0 when of Value line the of Slope variable nt) (independe Predictor variable ) (dependent Predicted where 2 2 = - - = - - = = = = = = + = å å å å å å å n x b y n x b y a x x n y x xy n b y x y a b x y bx a y c c c ( ) ( ) ( ) ( ) ( ) ( ) ( ) 2 2 2 2 å å å å å å å - - - = y y n x x n y x xy n r mean the from deviations standard of Number where MSE 0 : LCL . 4 MSE 0 : UCL . 3 MSE errors of on distributi the of deviation standard of Estimate 2. MSE. the Compute . 1 = - + = z z z s Unit 2: Discussion Introduction As production manager last unit in the Kibby and Strand simulation you gained insights into how raw materials were turned into finished goods. This unit you will learn more about the front end of the operational process employed by the company. Specifically, you will learn how to manage suppliers who provide the raw materials used in the production of the company’s textile products. Some challenges you will face are: 1) which suppliers provide the best quality raw materials; 2) which suppliers are the most reliable; and 3) which suppliers have the most competitive prices.  The simulation scenario will pose many opportunities for decision making and forecasting, and if you make a poor decision regarding suppliers it will impact the ability of Kibby and Strand to meet its contractual obligations, leading to dissatisfied customers. Since customer satisfaction weighs heavily on future contracts, you can’t simply make the best decision for the moment, but rather the best decision for the long haul. This scenario provides a realistic illustration of the issues textile companies face across the U.S. It’s extremely important that operations professionals have an above average comfortable level when it comes to establishing grounded assumptions and conducting and interpreting financial and operational forecasts. In its simplest form, forecasting is a process that represents an “educated guess”. In business, we use time series methods, the indicator approach, or regression analyses to forecast the nature of a situation or future values. The data we observe when forecasting fall into one of four types: trended patterns, seasonal patterns, cyclical patterns, or irregular patterns (Kros & Brown, 2013). Forecasting models are used to predict consumer demand, which, in turn, aids management in forecasting staffing requirements. In addition, to demand forecasts, management routinely engages in financial forecasting, which includes, but is not limited to: sales growth, economic predictions, and forecast future cash flows. In order to perform forecasts, it’s important that the management team signoff on the underlying assumptions used to complete these analyses, such as population growth and technology development. The following represents the typical steps one undertakes when preparing for and conducting a forecast (Investopedia, n.d.): 1. A problem or data point is chosen. This can be something like "will people buy a high-end coffee maker?" or "what will our sales be in March next year?" 2. Theoretical variables and an ideal data set are chosen. This is where the forecaster identifies the relevant variables that need to be considered and decides how to collect the data. 3. Assumption time. To cut down the time and data needed to make a forecast, the forecaster makes some explicit assumptions to simplify the process. 4. A model is chosen. The forecaster picks the model that fits the data set, selected variables and assumptions. 5. Analysis. Using the model, the data is analyzed and a forecast made from the analysis. 6. Verification. The forecaster compares the forecast to what actually happens to tweak the process, identify problems or in the rare case of an absolutely accurate forecast, pat himself on the back. Sources: Kros, J. F., & Brown, E. (2013). Health Care Operations and Supply Chain Management.  San Francisco, CA: John Wiley & Sons. http://www.investopedia.com/articles/financial-theory/11/basics-business-forcasting.asp (Links to an external site.)   Unit Learning Outcomes 1. Develop a plan for forecasting impacts to an organization’s bottom line. (CLO 1, 2, 4, and 7) 2. Demonstrate how to perform forecasting using data and statistics. (CLO 4 and 5) 3. Identify trends and patterns in data as they apply to forecasting. (CLO 1, 3, 5, and 7) 4. Develop a data collection plan that will permit the creation of an accurate and reliable forecasting model. (CLO 3, 4, and 5) Directions Accessing McGraw-Hill Connect Follow  these steps  to view the scenario. Initial Posting Go to McGraw-Hill Practice Operations to view the scenario. 1. Click the "McGraw-Hill Connect" tab in the course navigation menu. 2. Click the McGraw-Hill Practice Operations link. Students are to complete Module 3, Forecasting and Contracts (Scenario) in Practice Operations. Based on their observations in this scenario, and upon a careful review of the available literature, the student is to consider him or herself to be the Production Manager of Kibby and Strand, the company in the scenario. Create a forecasting plan to forecast production output for Kibby and Strand.  The plan should include forecasting objectives, the data to be used in forecasting, and the quantitative methods the staff is to use in creating the production output forecast. Instruction Guidance: It would be prudent to consider content covered in chapter 3 of the textbook; however, there are many other useful resources available on the Internet and in the literature to support the construction of your action plan.  This forecasting plan should be prepared as a single Microsoft Word document, and then attached to the unit discussion thread. There is no minimum or maximum in terms of the word count; however, the response should explicitly address all required components of this discussion assignment. The document should be prepared consistent with the APA writing style and reflect higher level cognitive processing (analysis, synthesis and or evaluation).